Holdout (Data Science): 편집 역사

2024년 12월 1일 (일)

• 최신이전 13:382024년 12월 1일 (일) 13:38‎ Fortify 토론 기여‎ 3,203 바이트 +3,203‎ 새 문서: '''Holdout''' in data science refers to a method used to evaluate the performance of machine learning models by splitting the dataset into separate parts, typically a training set and a testing set. The testing set, often called the "holdout set," is kept aside during model training and is only used for final evaluation to ensure unbiased performance metrics. ==How Holdout Works== The holdout method involves the following steps: *The dataset is split into two (or sometimes three... 태그: 시각 편집